Nonparametric Bayesian Methods for Large Scale Multi-Target Tracking

Author(s):  
Emily B. Fox ◽  
David S. Choi ◽  
Alan S. Willsky
Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4115 ◽  
Author(s):  
Feng Lian ◽  
Liming Hou ◽  
Bo Wei ◽  
Chongzhao Han

A new optimization algorithm of sensor selection is proposed in this paper for decentralized large-scale multi-target tracking (MTT) network within a labeled random finite set (RFS) framework. The method is performed based on a marginalized δ-generalized labeled multi-Bernoulli RFS. The rule of weighted Kullback-Leibler average (KLA) is used to fuse local multi-target densities. A new metric, named as the label assignment (LA) metric, is proposed to measure the distance for two labeled sets. The lower bound of LA metric based mean square error between the labeled multi-target state set and its estimate is taken as the optimized objective function of sensor selection. The proposed bound is obtained by the information inequality to RFS measurement. Then, we present the sequential Monte Carlo and Gaussian mixture implementations for the bound. Another advantage of the bound is that it provides a basis for setting the weights of KLA. The coordinate descent method is proposed to compromise the computational cost of sensor selection and the accuracy of MTT. Simulations verify the effectiveness of our method under different signal-to- noise ratio scenarios.


2014 ◽  
Author(s):  
Ba-Ngu Vo ◽  
Ba-Tuong Vo ◽  
Stephan Reuter ◽  
Quang Lam ◽  
Klaus Dietmayer

2019 ◽  
Vol 2019 (13) ◽  
pp. 127-1-127-7
Author(s):  
Benjamin J. Foster ◽  
Dong Hye Ye ◽  
Charles A. Bouman

2009 ◽  
Vol 28 (9) ◽  
pp. 2303-2305
Author(s):  
Xiao-gang WANG ◽  
Xiao-juan WU ◽  
Xin ZHOU ◽  
Xiao-yan ZHANG

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